论文标题
实例阴影检测使用单阶段检测器
Instance Shadow Detection with A Single-Stage Detector
论文作者
论文摘要
本文提出了一个新问题，实例的阴影检测，旨在检测影子实例和关联的对象实例，这些实例在输入图像中投射每个阴影。为了完成此任务，我们首先编译了一个新的数据集，其中包含掩码，用于阴影实例，对象实例和阴影对象关联。然后，我们设计一个评估度量标准，以定量评估实例阴影检测的性能。此外，我们设计了一个单阶段检测器，以端到端的方式执行实例阴影检测，在该模块中提出了双向关系学习模块和可变形的Maskiou头部，以直接学习阴影实例和对象实例之间的关系并提高预测面具的准确性。最后，我们在实例阴影检测的基准数据集上进行定量和定性评估我们的方法，并在光方向估计和照片编辑中显示我们方法的适用性。
This paper formulates a new problem, instance shadow detection, which aims to detect shadow instance and the associated object instance that cast each shadow in the input image. To approach this task, we first compile a new dataset with the masks for shadow instances, object instances, and shadow-object associations. We then design an evaluation metric for quantitative evaluation of the performance of instance shadow detection. Further, we design a single-stage detector to perform instance shadow detection in an end-to-end manner, where the bidirectional relation learning module and the deformable maskIoU head are proposed in the detector to directly learn the relation between shadow instances and object instances and to improve the accuracy of the predicted masks. Finally, we quantitatively and qualitatively evaluate our method on the benchmark dataset of instance shadow detection and show the applicability of our method on light direction estimation and photo editing.
